Current face forgery detection methods based on deep learning usually use softmax loss to supervise network training.
However, the learned features supervised by softmax loss are essentially not discriminative enough,
since softmax loss only focuses on finding a decision boundary to separate different classes.
The intra-class compactness and inter-class separability are not explicitly considered.
Obviously, deep metric learning is a promising solution. However, most metric learning methods, such as triplet loss [1]} and center loss [2]},
usually indiscriminately compress intra-class variations of natural and manipulated faces in embedding space.
While feature distributions of manipulated faces vary from one manipulation method to another.
That is because GAN fingerprints [3]}, manipulated region, and other unique operations, \(\) , post-processing techniques, lead to specific artifacts
for each manipulation method.
For example, Deepfakes [4]} generates the whole face while NeuralTextures [5]}
only manipulates the mouth region of the target person. Intuitively, their distribution in the embedding space should be evidently different.
A side evidence would be that the generalization ability of features learned by supervised learning is significantly weakened on unseen manipulation methods.
This implies that features learned by supervised learning are highly related to manipulation methods.
The feature differences of samples generated by different manipulation methods make it difficult to aggregate all the manipulated faces.
Therefore, indiscriminately constraining intra-class compactness in embedding space usually leads to a sub-optimal solution due to optimization difficulty and even damages the performance owing to overfitting.
In order to solve this problem, we devise a novel single-center loss.
